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研究生:何碩俞
研究生(外文):Shuo-Yu He
論文名稱:應用基因演算法於配電系統饋線重組以降低電壓驟降
論文名稱(外文):Feeder Reconfiguration in Distribution Systems for Voltage Sag Reduction by Genetic Algorithms
指導教授:洪穎怡洪穎怡引用關係
指導教授(外文):Ying- Yi Hong
學位類別:碩士
校院名稱:中原大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:70
中文關鍵詞:電壓驟降Prufer number葉片節點編碼基因演算法簡化型之負載潮流方程組
外文關鍵詞:prufer number encodinggenetic algorithmsvoltage sagssimplified power flow equations
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在配電系統中,當雷擊、鹽害或人為引起之事故,導致系統發生電壓驟降,可能造成電力設備欠壓,保護電驛動作而造成電力中斷,或影響用戶端設備正常運轉,尤其對電力品質反應較靈敏之高科技產業受害最大。
本論文應用基因演算法於配電系統饋線重組之問題上,以降低系統的損失與電壓驟降,並加入模糊多目標規劃方法來求解。在本論文中亦利用一組簡化型的負載潮流方程組,以用來求解負載潮流問題,此分析方法可直接從線路末端計算至供電端,即可求解整個負載潮流資料,而摒除傳統方法於求解時所需之反覆疊代。此外,在基因演算法方面,針對配電系統之放射狀樹枝架構,利用Prufer number 葉片節點編碼方法於染色體的編碼上,使其在經歷交配、突變運算之後仍然能夠保持放射狀架構。
本文分別以一個16個匯流排與33個匯流排的配電系統,進行電腦模擬測試,其結果顯示本文所提之基因演算法,可有效地求得配電系統饋線重組之近似最佳解。
Voltage sags are usually caused by shorts circuits, overloads, and starting of large motors. The interest in voltage sags is mainly due to the problems they cause on several types of equipment: adjustable-speed drives, process control equipment, and computers.
In this thesis, a method based on the Genetic algorithms and decision making for multi-objective programming problems to study the reconfiguration in distribution systems for loss and voltage sag reduction is proposed. A set of simplified power flow equations is used for approximate loss calculation. Then the Prufer number is used for encoding a tree structure on the Genetic algorithms.
A 16-bus system and a 33-bus system are used as test systems for showing the applicability of the proposed method.
摘要..................................I
Abstract..............................II
誌謝..................................III
目錄..................................IV
圖目錄................................VII
表目錄................................IX
第一章 緒論...........................1
1.1 研究背景與動機....................1
1.2文獻回顧...........................2
1.3研究步驟...........................3
1.4研究目標...........................4
1.5本論文之貢獻.......................4
1.6論文架構概述.......................5
第二章 問題描述與數學表示式...........7
2.1 配電系統饋線重組問題..............7
2.2 配電系統負載潮流方程組............8
2.2.1 前言............................8
2.2.2 簡化型負載潮流方程組............9
2.3 電壓驟降之定義與成因..............13
2.3.1 前言............................13
2.3.2 電壓驟降之計算..................13
2.4 數學表示式........................15
2.4.1 最小化損失表示式................15
2.4.2 最小化壓降表示式................17
2.4.3 同時損失與壓降最小化表示式....19
第三章 數學背景.......................20
3.1 基因演算法之簡介..................20
3.2 基因演算法預備作業................21
3.2.1 適應函數........................21
3.2.2 Prufer number編碼與解碼方式.....21
3.2.3 初始族群........................27
3.3 基因演算法之工作原理..............27
3.3.1 染色體的複製(Reproduction)......28
3.3.2 染色體的交配(Crossover).........28
3.3.3 染色體的突變(Mutation)..........31
3.4 處罰函數(Penalty function)........31
3.5 模糊集合理論(Fuzzy set theory)....33
3.6 模糊線性規劃......................35
3.7 最佳化問題之求解過程..............38
第四章 模擬結果.......................39
4.1 十六個匯流排之模擬結果............39
4.1.1 以系統損失為單一目標............40
4.1.2 以壓降為單一目標................43
4.1.3 以系統損失與壓降同時進行雙目標規劃...45
4.2 三十三個匯流排之模擬結果..........49
4.2.1 以系統損失為單一目標............52
4.2.2 以壓降為單一目標................58
4.2.3 以系統損失與壓降同時進行雙目標規劃...61
4.3 模擬結果討論......................66
第五章 結論...........................67
參考文獻..............................68
〔1〕林士煥,沈鎮南,凌拯民,張原彰,黃昆松,黃川桂,「電力系統分析」,高立圖書有限公司,民國87年7月。〔2〕Ying-Tung Hsiao and Ching-Yang Chien, “Enhancement of Restoration Service in Distribution Systems Using a Combination Fuzzy-GA Method,” IEEE Trans. on Power Systems, Vol. 15, No. 4, 2000, pp. 1394-1400.〔3〕Mistsuo Gen and Runwei Cheng, “Genetic Algorithms and Engineering Design,” John Wiley & Sons, New York, 1997.〔4〕Prufer, H., Neuer beweis eines satzes uber permutation, Arch. Math. Phys., vol. 27, pp. 742-744, 1918.〔5〕Shiena, S., Implementing Discrete Mathematics Combinatorics and Graph Theory with Mathematics, Addision-Wesley, MA, 1990. 〔6〕H.-J. Zimmermann,“FUZZY SET THEORY─AND ITS APPLICATIONS SECOND EDITION,茂昌圖書有限公司,民國80年。〔7〕Hong-Chan Chin and Kun-Yuan Huang, “A Simple Distribution Reconfiguration Algorithm for Loss Minimization,” IEEE Trans. on Power Systems Technology, Vol. 2, No. 2, 2000, pp. 607-611.〔8〕Hoyong Kim, Yunseok Ko and Kyung-Hee Jung, “Artifical Neural-Network Based Feeder Reconfiguration for Loss Reduction in Distribution Systems,” IEEE Trans. on Power Delivery , Vol. 8, No. 3,July 1993, pp. 1356-1366.〔9〕Sang-Yun Yun, Jung-Hwan Oh, Seong-Jeong Rim and Jae-Chul Kim, “Mitigation of Voltage Sag Using Feeder Transfer in Power Distribution System,”Engineering Society Summer Meeting, Vol. 3, 2000, pp. 1421-1426.〔10〕T. Taylor and D. Lubkeman, “Implementation of Heuristic Search Startegies for Distribution Feeder Reconfiguration,” IEEE Trans. on Power Delivery, Vol. 5, No. 1, 1990, pp. 239-245.〔11〕S.K. Goswami and S.K. Basu, “A New Algorithm for the Reconfiguration of Distribution Feeders for Loss Minimization,” IEEE Trans. on Power Delivery, Vol. 7, No. 3, 1992, pp. 1484-1491.〔12〕H.D. Chiang and R.J. Jumeau, “Optimal Network Reconfigurations in Distribution Systems: Part 1: A New Formulation and A Solution Methodology,” IEEE Trans. on Power Delivery, Vol. 5, No. 4, 1990, pp. 1902-1909.〔13〕W.-M.Lin, F.-S.Cheng and M.-T.Tsay, “Distribution Feeder Reconfiguration with Refined Genetic Algorithm,” IEE Proc-Gener. Trans. Distrib., Vol. 147, No.6, 2000, pp. 349-353.〔14〕Mesut E. Baran and Felix F. Wu, “Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing,” IEEE Trans. on Power Delivery, Vol. 4, No. 2, 1989, pp. 1401-1409.〔15〕Roger C. Dugan, Mark F. McGranaghan and H. Wayne Beaty, “Electrical Power Systems Quality,” McGraw-Hill, New York, 1996.〔16〕Larry Conrad, Kevin Little, Cliff Grigg, ”Predicting and Preventing Problems Associated with Remote Fault-Clearing Voltage Dips,” IEEE Trans. on Industry Applications, Vol. 27, No. 1, 1991, pp. 167-172.〔17〕林信成,彭啟峰,「Oh! Fuzzy 模糊理論剖析」,第三波文化事業股份有限公司,民國83年10月。〔18〕L.A. Zadeh, ”Fuzzy Sets,” Information and Control, Vol. 8, No. 1, 1965, pp. 338-353.〔19〕王欽輝,侯志陞,「FUZZY工學」,全華科技圖書股份有限公司,民國81年12月。〔20〕H.J. Zimmermann, ”Fuzzy Sets Theory and Its Applications,” Kluwer Academic Publishers, 1991,Boston.〔21〕Masatoshi Sakawa, Kosuke Kato, Hideaki Sunada and Toshihiro Shibano, “Fuzzy Programming for Multiobjective 0-1 Programming Problems Through Revised Genetic Algorithms,” European Journal of Operational Research, 1997, pp. 149-158.
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